Recognizing text in an image is one of the important operations in automated processing of images of natural language texts. Identifying graphemes from an image can be performed using deep neural networks. Accurately identifying and classifying graphemes from images of documents, however, can be complicated by neural networks that include a large number of layers. Additionally, each layer of such a neural network may be called up to analyze an image based on a large number of possible target graphemes. This can require significant resources in order to extract information accurately and in a timely manner.